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agent
behavior
emulator
environment
human_modeling_utils
openai_gym
survey
.gitignore
LICENSE
README.md
constant.py
controller.py
main_behavior_summary.ipynb
main_emulator_create.py
main_emulator_fake_response.py
main_emulator_resume.py
main_flag_workers.sh
main_get_classifier_data.ipynb
main_interative_learning.py
main_openai_gym_ray.py
main_openai_gym_tensorforce.py
main_profile_mturk_data.ipynb
main_profile_mturk_data_2.ipynb
main_teacher_student_learning.ipynb
plot_agent_performance.ipynb
plot_agent_performance_2.ipynb
plot_baseline_performance.ipynb

README.md

Nurture-UbiTtention18

In our Nurture project, we conducted two simulation-based experiments to study how can we use reinforcement learning algorithms to model user interruptibility and schedule notifications based on user context. Our paper is accepted in UbiTtention workshop 2018. The title of the paper is "Nurture: Notifying Users at the Right Time Using Reinforcement Learning", co-authored by Bo-Jhang Ho, Bharathan Balaji, Mehmet Koseoglu, and Mani Srivastava.

Import the dataset

Please contact the PI Mani Srivastava for the dataset. Once you get the dataset, you have to make a couple of symbolic links so that the codebase can be aware of these datasets. Trace this repository and it will be mentioned in README.md in different folders.

For the current folder, please make the following symbolic link:

ln -s /path/to/figure/folder export_figures

You can’t perform that action at this time.